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Conference Paper

Learning Depth From Stereo

MPS-Authors
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Sinz,  F
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83845

Candela,  JQ
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83791

BakIr,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83919

Franz,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://link.springer.com/content/pdf/10.1007%2F978-3-540-28649-3_30.pdf
(Publisher version)

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Citation

Sinz, F., Candela, J., BakIr, G., Rasmussen, C., & Franz, M. (2004). Learning Depth From Stereo. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 245-252). Berlin, Germany: Springer.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D7EB-B
Abstract
We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two
different cameras: 1.~The classical photogrammetric approach
explicitly models the two cameras and estimates their intrinsic
and extrinsic parameters using a tedious calibration procedure;
2.~A generic machine learning approach where the mapping from
image to spatial coordinates is directly approximated by a Gaussian Process regression. Our results show that the generic
learning approach, in addition to simplifying the procedure of
calibration, can lead to higher depth accuracies than classical
calibration although no specific domain knowledge is used.